Chapter 2: Literature Review

DOI: 10.1002/ace.20521

R E S E A R C H A R T I C L E

Incorporation of artificial intelligence in healthcare professions and patient education for fostering effective patient care

Andrea Mucci Wendy M. Green Lilian H. Hill

Cleveland State University

Correspondence Andrea Mucci Email: [email protected]

Wendy M. Green Email: [email protected]

Lilian H. Hill. Email: [email protected]

Abstract Artificial intelligence (AI) influences many aspects of modern life and has multiple applications in the deliv- ery of healthcare. AI is designed to mimic human capabilities including pattern recognition, data anal- ysis, and decision-making and perform tasks more efficiently. It is capable of detecting patterns in large datasets that might elude human beings. AI has potential to improve patient care, patient safety, dis- ease diagnosis and treatment; public health; health research; health administration; and the daily work of health professionals. In 2019, the US National Academy of Medicine emphasized the need for “physi- cians, nurses, and other clinicians, data scientists, health care administrators, public health officials, pol- icy makers, regulators, purchasers of health care ser- vices, and patients to understand the basic concepts, current state of the art, and future implications of the revolution in AI and machine learning.” Therefore, it is incumbent on adult educators to incorporate AI training into health professions education and patient education.

INTRODUCTION

Artificial intelligence (AI) has the potential to dramatically change healthcare, including but not limited to improving patient care, patient safety, disease diagnosis and treat- ment; public health; health research; health administration; and daily workflow of health professionals. In 2019, the US National Academy of Medicine emphasized the need for “physicians, nurses, and other clinicians, data scientists, health care administrators, public

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52 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

health officials, policy makers, regulators, purchasers of healthcare services, and patients to understand the basic concepts, current state of the art, and future implications of the revolution in AI and machine learning” (Lomis et al., 2021, p. 1). Therefore, it is incumbent on adult educators to incorporate AI training into health professions education and to be aware of how it can augment patient education. The purpose of this chapter is to exam- ine how evolving AI technology will influence patient care. While the emphasis is on the undeniable potential of this technology, we also highlight some of the constraints which should be addressed. After defining some basic terms and providing an overview of AI, we will briefly discuss the importance of ensuring adequate knowledge of AI for health care providers and then we will focus on the intersection of AI and the patient experience, including education.

DEFINITIONS

∙ Artificial intelligence refers to the ability of machines or software to perform tasks that normally require human intelligence such as pattern recognition, data analysis, and decision-making.

∙ Health professions education refers to advanced academic programs designed to prepare individuals for careers in healing professions, including medicine, nursing, den- tistry, pharmacy, allied health, and other professions. The educational process includes extensive academic courses and apprenticeships under the supervision of qualified professionals in patient-care settings such as hospitals, clinics, and the community.

∙ Patient education involves providing guidance to patients about ways to prevent and manage medical conditions. This education occurs during health HCP-patient interac- tions, structured courses, print material, video, online, public health communications, and other mechanisms.

ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) operationalizes the algorithmic steps in smart machines that perform tasks usually associated with human intelligence such as “learning, adapting, syn- thesizing, self-correction, and use of data for complex processing” (Popenici & Kerr, 2017, para. 3). Machine learning is an application of AI in which large datasets are analyzed to detect patterns that might elude human beings. Generative AI is a type of artificial intelli- gence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

AI types can be categorized, based on capability or functionality. One capability-based categorization is weak, general, and strong. Narrow or weak AI can perform single-specific tasks such as facial recognition, self-driving cars, searching the internet, translating lan- guages, or making grammatical or spelling change recommendations. General or strong AI can perform tasks in a human-like manner (Sun et al., 2023). A functionality-based categorization asserts three categories of AI: (a) large language models (LLM), (b) learn- ing analytics in which personalized learning is tailored for individuals, and (c) big data, meaning using large data sets to conduct comparative analysis between groups of peo- ple (Ellaway et al., 2014). Figure 1 presents a categorization by Lomis et al. (2021) who provided an excellent overview of the role of AI in health professions education (HPE), and distinguishes natural language processing, machine learning, and other types of AI (Figure 1). No matter how you conceptualize it, the field of AI is complex, growing, and rapidly being integrated into multiple fields of professional practice, including healthcare.

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F I G U R E 1 AI capability (adapted from Lomis et al., 2021).

ADULT LEARNING, HEALTHCARE PROFESSIONS EDUCATION, AND PATIENT EDUCATION

Adult learning principles can be applied to the training of healthcare providers (who require lifelong continuing medical education allowing them to maintain current and effective knowledge and skills) and to the education of patients and caregivers. Patients engage in continuous learning as disease prevention and management require knowledge and skills that will evolve throughout the lifespan and/or the disease process. Diagno- sis of a serious or chronic condition engages individuals in learning about the condition, and its often-multifaceted management, and prognosis. Education and health literacy are necessary in order for patients to comprehend health instructions, make personal health decisions, and care for family members. Health literacy incorporates personal and organi- zational health literacy. This paired definition refers to the degree to which individuals can locate, comprehend, and apply health information to make appropriate health decisions and that healthcare organizations provide understandable resources that will facilitate personal health literacy. Importantly, the definition addresses the responsibility of health- care organizations to address health literacy (Centers for Disease Control and Prevention, 2020). It is also important to consider digital literacy which is defined as “the ability and knowledge needed to access and operate internet-connected devices, to successfully use commonly available software, and to navigate and utilize online resources in order to effec- tively communicate and complete social and work-related tasks in a virtual space” (Barbara Bush Foundation for Family Literacy, 2022.)

ADULT LEARNING THEORIES

Adult learning theories that relate to the application of AI in healthcare include experiential, embodied, transformational learning, and situated cognition (Merriam & Baumgartner, 2020). Learning from the experience of illness requires paying attention and making meaning of experience. Embodied learning recognizes that learning from experi- ence involves the whole person, including the intellectual, physical, and emotional selves.

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54 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

Diagnosis of an illness may provide a disorienting dilemma that prompts critical reflec- tion and transformation of values and decision-making. In their study of African American women diagnosed with diabetes, Ntiri and Stewart (2010) suggested that transformative learning interventions may serve as an effective method for improving health literacy and promoting effective self-management practices.

Situated cognition theory indicates that adult learning takes place in community, using tools in context within a particular activity (Merriam & Baumgartner, 2020). Situated learning involves a shared process of perceiving and acting, authentic learning situa- tions, multiple exposures to realistic situations, and knowledge embedded in the actions of individuals and groups (Artino, 2013). Thus, “cognition, knowing, and learning all take place as interactions between people and their environment” (Artino, p. 177). Patients and healthcare providers benefit from learning in situations that are contextualized. A person diagnosed with an illness will engage with health professionals and be advised about treatment and necessary changes to their health practices. This may require dras- tic lifestyle changes that will affect people in their family and social circle. Likewise, to best advise patients to make changes that benefit them, healthcare providers must learn about patients’ lifestyle and social situations. For example, a study applying situated learn- ing theory to develop self-efficacy in patients with diabetes illustrated the effectiveness of web-supported tools in a cooperative work model to enhance self-management skills and self-efficacy. The authors noted “individuals must learn through solving authentic prob- lems in the context of a personalized social and physical environment rather than from classroom instruction” (Hsu et al., 2016, p. 64).

In medical education, problem-based learning requires students to consider realistic situations that are often ill-defined and have multiple solutions, similar to real clini- cal practice. Clinical education engages health professions students through supervised apprenticeships in authentic environments including clinics, hospitals, and medical cen- ters. This educational approach requires learners to meaningfully engage in actions embedded in their daily practice with the goal of developing effective practitioners.

INTEGRATION OF AI INTO HEALTHCARE

Learning about AI and its applications in healthcare presents a new challenge that involves medical, technical, and social knowledge. Lomis et al. (2021) indicated that AI is being integrated into healthcare in the following dimensions: disease prevention, diagnosis, and treatment; support of care delivery; patient engagement, and research and development (Figure 2). Each dimension involves information to be learned within social situations and involves tools used in authentic contexts. Similarly, health professionals will learn about tools used in disease diagnosis and treatment and apply them to patients in clinical settings.

AI IN HEALTH PROFESSIONS EDUCATION

As AI becomes integrated into society including in the healthcare applications out- lined in the previous section, we reflect on how this will impact health professions education (HPE). To employ AI effectively in their practice, providers need an under- standing of how AI is integrated into aspects of work flow and patient care. It is incumbent on health professions educators to provide continuing medical education that supports provider development. For educators, AI is used to understand the effi- cacy of educational programming and can identify areas in need of improvement. AI

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F I G U R E 2 AI Uses and application in healthcare (adapted from Lomis et al., 2021).

can monitor student progress, highlight areas in need of development, and compare learner groups across systems or to specific professional benchmarks (Shankar, 2022). Although there are many challenges and barriers including ethical and medicolegal, Lomis et al. (2021) suggest, “embracing AI tools as partners will result in augmented intelligence of the entire healthcare system and the individuals within it” (p. 2). Edu- cators who are able to effectively harness the power of AI will be able to better align content with learner needs in conjunction with understanding the overall program efficacy thus strengthening the provider’s knowledge and confidence in using AI. Updat- ing educational systems will require incorporating new technologies, reorganization of the classroom, curriculum, and professional development (González-Pérez & Ramírez- Montoya, 2022).

AI IN PATIENT EDUCATION

Patient education helps individuals to learn about their health condition(s) and diag- nosis, make informed treatment choices, and practice preventive measures. AI can play a significant role in predicting, treating, and monitoring patient’s disease management and progression. Informed patients are more likely to adhere to treatment regimens and achieve better health outcomes. A significant role for AI in healthcare is providing health information and patient education that is personalized and interactive (Alowais et al., 2023). Functions AI can provide include personalized guidance, virtual assistance, remote monitoring/guidance, health education, predictive analytics, and language translation and adaptation. Patients do not need to know how AI functions. Rather, patients would benefit from a better understanding of how AI is used as a tool to increase positive health outcomes and inform their plans for better health.

AI can provide patients with interactive information tailored to their specific conditions and medical needs. AI can be integrated into digital health education platforms that offer a comprehensive range of resources, including videos, articles, and interactive modules. These resources can also be linked to authoritative information sources such as Medline and PubMed. These platforms can deliver evidence-based information in an easily under- standable format, enabling patients to access educational content at their convenience. Patients can receive educational resources tailored for their specific condition, its urgency, and patients’ ability to improve health outcomes (Davenport & Kalakota, 2019). One

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56 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

example is the application of AI to diabetes self-management, the cornerstone of achiev- ing good control of diabetes. In their review of this topic, Li et al. (2020) examine multiple applications of AI including various approaches to lifestyle education, glucose monitoring, insulin dosing, and monitoring of diabetes complications. This information can adapt to changes in a patient’s health condition resulting from treatment. Generative AI, such as that found in ChatGPT, can be effective for delivering patient information (Heath, 2023), helping people understand their diagnosis and treatment options, monitoring their symptoms and adherence, providing feedback and encouragement, and answering their questions (Alowais et al., 2023).

AI-enabled remote monitoring devices can track a person’s health metrics, such as vital signs, medication adherence, and lifestyle habits. Not only can this provide direct feedback to the individual via online platforms (i.e., a person with diabetes wearing a continuous glucose monitor can receive alerts predicting an upcoming low blood sugar or feedback about the average trends in their blood sugars), but AI can also allow the health care provider to assess the severity of a patient’s condition. For example, a diabetes team can remotely monitor CGM (continous glucose monitoring) data on either an individual or a population level to provide individualized care, and/or triage care. Furthermore, AI can advise a patient to treat a condition at home or seek emergency services. For example, Chow et al. (2023) described a conversational agent used during pandemic isolation in Australia to assess patients’ COVID symptoms using daily phone calls. The script employed closed-end questions such as “are you short of breath?” and affirmative patient responses could be flagged to warrant a referral to human clinicians. This practice provided daily monitoring, kept low-risk patients in their homes, and reserved clinician time for seriously ill patients. By leveraging data mining, machine learning, and AI, healthcare providers can predict potential health risks and complications for individual patients. This predictive insight can be used to educate patients about proactive measures they can take to mitigate these risks and promote better health outcomes (Alowais et al., 2023).

AI-supported virtual assistants can help patients with tasks such as reminding patients to take their medications, scheduling doctor appointments, and monitoring vital signs (Alowais et al., 2023). Virtual assistants can collect health information and report it to healthcare providers, thereby reducing their workload and improving patient outcomes. They can also be designed to nudge patients to change their behavior to follow clinical recommendations (Davenport & Kalakota, 2019).

In a clinical encounter, AI can augment the communication between a healthcare provider and the patient. For instance, since providers typically spend over 30% of their time documenting in the electronic health record (EHR), there are now AI-based appli- cations that “use neural networks to map patient-physician conversations into a note in the EHR (AI-based speech-to-text system)” or others that can transform conversations into prescription orders and referrals (Kutty, 2021). AI can also translate health information into different languages and adapt its reading difficulty to patients’ literacy levels (Heerschap, 2023). Medical information is unfamiliar to many people, and even well-educated people prefer health information that is clear and understandable.

ADVANTAGES AND CONCERNS OF AI IN HEALTHCARE

There are many advantages of AI including “broad coverage, low cost, high efficiency, portability, diversity, and service-end productivity” (Li et al., 2020, p. 193). Integrating AI into patient education enhances the accessibility of healthcare information, pro- motes patient engagement, and empowers individuals to take an active role in managing their health. However, it is essential to ensure AI-driven solutions prioritize accuracy,

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NEW DIRECTIONS FOR ADULT AND CONTINUING EDUCATION 57

F I G U R E 3 AI concerns (adapted from Stafie et al., 2023).

patient privacy, security, and ethical considerations throughout the educational process (Figure 3).

LLMs such as ChatGPT use artificial intelligence to respond to queries based on algo- rithms. Although the conversation may appear “human-like,” chatbots work by pulling information from datasets to identify patterns related to tasks such as diagnosis and making treatment recommendations. For clinical applications, the datasets the system consumes to “learn” must be carefully selected and modified as clinical knowledge is updated. Chatbots do not read in a human sense. They do not engage in meaning-making or being critical of information. Instead, they have been known to “hallucinate” and provide inaccurate or biased information (Shumaker, 2024).

Algorithms and artificial intelligence are said to be a “black box,” meaning that AI- systems are not designed to reveal their inner operations and users do not necessarily have access to information about how they work. This lack of information can result in dimin- ished trust for both health providers and patients. Although AI used in medical settings requires U.S. Federal and Drug Administration approval which assesses new technologies based on risk to patient safety, predicate algorithms, and human input, there are concerns. The lack of transparency complicates evaluation of AI-supported drugs, medical devices, and healthcare systems. In cases where algorithmic risks are high, such as diagnostic tools where a misdiagnosis would have severe consequences and where human input is minimal, a premarket approval process is conducted. In order to be approved, there must be solid evidence the tool is safe and effective from both non-clinical and clinical studies (Stafie et al., 2023). However, AI development is still in its infancy and problems with accuracy, adaptability, and clinical applicability remain.

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58 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

As the use of AI in healthcare increases, the importance of robust legislation to pro- tect patient privacy is vital (Alowais et al., 2023). Some suggest that aggregated health data should be used for the collective good and bioethicists are questioning whether the concept of data privacy is becoming obsolete. Pyrrho et al. (2022) concluded that:

treating data as [a] common good seems more like a facade for big economic interests to continue to appropriate and trade privacy as a commodity … [R]egulations need to focus their efforts on combating the opacity of digital activities undertaken by companies that produce and use aggregated data. (p. 58).

Stafie et al. (2023) argue ensuring data privacy is essential to safeguard the privacy of patients as health information is personal and sensitive. In 2023, in response to privacy concerns, the US government established AI safety and security standards. These standards were designed to advance equity and civil rights and to advocate for consumers and work- ers. Although AI has the propensity to make intended and unintended large-scale changes globally, as of early 2024, the US Congress had failed to pass legislation to regulate the use of AI (The White House, 2023).

AI algorithms can perpetuate biases present in the data used to train them, leading to disparities in quality and accessibility. Ensuring AI systems are designed to be fair and unbiased is essential for providing equitable healthcare and patient education. If racism or other forms of discrimination has historically been present, the algorithm is likely to perpetuate it unless specific action is taken to prevent it. Similarly, if certain populations are routinely left out of data, they will continue to be neglected. Algorithms used in AI may be based on human prejudice, bias, and even misunderstandings reflected in large datasets. However, human beings can change their thinking and transform their belief systems whereas, AI does not have a moral sense. Human oversight is needed to correct problems (Dickson, 2020)

AI-supported patient education resources must be presented so they are easily under- standable and accessible to patients with varying levels of health literacy. While patients do not need to understand how complex medical tests and treatments work, they do need clear interpretation and explanation of their meaning. Complex medical terminology and technical language can interfere with patients’ understanding. Failure to address health literacy needs limits the effectiveness of AI-based educational tools.

While AI can provide valuable educational resources, it cannot replace the importance of human interaction and empathy in patient education. Ensuring that AI is used to supplement, rather than replace, human-led educational initiatives is vital for maintain- ing the human touch in healthcare. A study conducted by Yale University indicated that patients’ perceptions of AI in healthcare were generally positive about its potential to improve healthcare; however, concerns were expressed regarding its potential for misdiag- nosis, privacy breaches, reduced time with clinicians, and increased costs (Gaudette, 2022). In contrast, Robertson et al. (2023) concluded that significant patient resistance exists and could be described as “algorithm aversion” or “robophobia” (p. 13). This resistance is more pronounced when AI is involved in physician-patient interactions (Davenport & Kalakota, 2019).

Significant costs are incurred in the development and implementation of AI-based sys- tems which limits access in resource-poor areas (Stafie et al., 2023). Often these costs are assumed by commercial for-profit companies, meaning that their intent is not consistent with those of healthcare professionals. Researchers have identified a development-to- implementation gap of AI that describes a mismatch between the goals for which AI is developed by manufacturers and the needs of practitioners and patients in practice. To

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address these problems, it is important to apply the lessons learned from clinical trials to AI development: seeking input from providers and participants, informing participants, protecting patient safety, assessing usability, paying attention to technical requirements, using stringent research methods, and clear and transparent reporting. The unique knowl- edge of health providers, patients, and the public can be used to develop effective and useful AI tools (The Decide AI Steering Group, 2021).

Addressing these concerns requires collaborative efforts between healthcare profession- als, policymakers, and technology developers to establish guidelines, protocols, and best practices for the responsible integration of AI in healthcare, healthcare education, and patient education. By prioritizing patient well-being, privacy, and inclusivity, healthcare stakeholders can leverage AI to enhance the quality and accessibility of patient education while mitigating potential risks and challenges.

AI AT THE INTERSECTION OF HEALTHCARE PROFESSIONALS AND PATIENTS

We have discussed how AI can affect healthcare, health professions, and patient education individually, but it is also important to consider how AI will mitigate interactions and rela- tionships between healthcare professionals and patients. At best, AI applications are tools for healthcare providers and patients. However, AI systems are unable to provide empathy, personal connection, and trust that are important aspects of effective patient care. AI tools cannot emulate healthcare professionals’ clinical intuition which is based on significant experience. Additionally, they do not consider social dimensions of health such as indi- vidual, lifestyle, sociocultural, and environmental conditions (Hill, 2016) unless they are specifically designed to do so. Although the use of AI conveys many benefits, “computers will never substitute for a self-reflective medical expert who is aware of the strengths and limitations of human beings and of an environment characterized by information over- load” (de Leon, 2018, p. 133). Rather than being replaced by AI, people can employ AI to support and enhance healthcare professionals’ abilities (Stafie et al., 2023).

As the use of AI-based technologies becomes more prevalent in healthcare, there is the potential to change the health profession-patient relationship (Paranjape et al., 2019). Physicians will be expected to identify AI tools to facilitate patient care, thus emphasizing the need for physicians’ fluency in AI. Karaca et al. (2021) advocate for presenting AI in a way that supports students’ understanding of clinical problems and reasoning. In addi- tion, they argue the curriculum must include ethical and legal issues regarding AI as well as a basic knowledge of AI functions and techniques. If these curricular developments are made in medical school, the knowledge and use of AI will, over time, be incorporated into the medical practice. Transforming curriculum and training programs has the propensity to “not only improve learning effectiveness and reduce costs of HPE, but also to provide a better learner experience that ultimately leads to higher quality care of patients” (Lomis et al., 2021, p. 10). However, there are ongoing debates regarding what AI-based tech- nology information should be included in already content-heavy medical education and continuing professional development curricula.

Adult educators are at the forefront when conceptualizing the use of AI for both patient and health professions education. As the number of health professions educators trained in both their healthcare specialty and adult education grows, there is an opportunity for collaborative processes that address the complex challenges of health professions and patient education. Educators, drawing on adult learning theory, are better able to align teaching methods with the desired learning outcomes when designing AI-driven learning activities. A clear alignment fosters more robust learning environments that lead to desired

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60 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

learning outcomes. Additionally, as adult educators engage in the ethical questions of AI use in learning environments they will help to create guidelines that protect learner privacy, limit data collection, and ensure data security (Masters, 2023).

THE FUTURE OF AI AND HEALTHCARE

The AI tools, including wearable monitoring devices, natural language processing that sim- ulates conversation, decentralized data platforms, real-time data analytics, and robotics are expected to revolutionize healthcare delivery, management, and access. Davies et al. (2021) describe three trends supported by AI:

∙ Patients will have increased control of their health because they will be able to control how their personal data is gathered, stored, and used. Additionally, they can choose how and when to engage with healthcare providers.

∙ Healthcare providers will be able to work more effectively and provide personalized, proactive care.

∙ Emerging technologies are creating opportunities to improve accessibility of healthcare services and transform the lives of patients.

CONCLUSION

As science and technology including AI continue to evolve we will find it in all aspects of the world we live in, including healthcare. While the many benefits, including efficiency, support its increasing presence in healthcare, it is important that all stakeholders including administration, healthcare providers, and patients are aware of not only its affordances, but also its constraints. Effective care for patients will continue to require human interaction; the goal is to allow technology to augment these relationships and the care being provided rather than hinder them.

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62 INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PROFESSIONS

How to cite this article: Mucci, A., Green, W. M., & Hill, L. H. (2024). Incorporation of Artificial Intelligence in Healthcare Professions And Patient Education For Fostering Effective Patient Care. New Directions for Adult and Continuing Education, 2024, 51–62. https://doi.org/10.1002/ace.20521

A U T H O R B I O G R A P H I E S

Andrea D. Mucci, MD, MASc, MEd is a pediatric endocrinologist and an assistant pro- fessor of Pediatrics at Cleveland Clinic Lerner College of Medicine of Case Western Reserve University. Her research in adult education includes medical education cur- riculum design, assessment, and evaluation, with special interests in digital learning and artificial intelligence.

Wendy M. Green, PhD, is an associate professor at Cleveland State University. Her research focuses on adult learning and health professions education, employee resource groups, organizational culture, and diversity.

Lilian H. Hill, PhD, is professor emerita of Adult Education at the University of South- ern Mississippi School of Education. Her research focuses on adult literacy and health education, digital literacy, and assessment and evaluation.

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